SpringerLink
Log in

Emerging artificial intelligence applications: metaverse, IoT, cybersecurity, healthcare - an overview

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The term "artificial intelligence" (AI) refers to "smart" high-tech that is mindful of and able to learn from its surroundings. It is the most revolutionary technology that humans have ever created. Common AI approaches involving machine learning and deep learning techniques can be effectively applied to resolve today's various cybersecurity issues. Furthermore, the metaverse is all about how people communicate and engage with one another through technology. This survey explores the role of AI with its emerging applications and their various technologies, such as the metaverse, healthcare, IoT, gaming, and many more. To determine the strengths, flaws, opportunities, and risks that are inherent in artificial intelligence technologies, using an extensive literature survey, the SWOT (Strengths, Weaknesses, Opportunities, and Threats) assessments have been undertaken in this survey paper. Finally, the survey paper summarises the current state of knowledge of AI applications and discusses the findings present in recent research to ensure a favourable change in artificial intelligence advances and applications. Some technical AI challenges, like high-speed, high-performance hardware and reducing the amount of training data, etc., are also discussed with future scope.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Goyal D, Goyal R, Rekha G, Malik S, & Tyagi AK (2020) Emerging trends and challenges in data science and big data analytics. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). IEEE, 1–8. https://doi.org/10.1109/ic-ETITE47903.2020.316

  2. Apoorva R, Arasa D, Jamadade S (2018) A survey on artificial intelligence. Int J Eng Res Technol (IJERT) NCESC – 6(13):1–6. https://doi.org/10.17577/IJERTCONV6IS13220. (ISSN:2278-0181)

    Article  Google Scholar 

  3. Batarseh FA, Freeman L, Huang CH (2021) A survey on artificial intelligence assurance. J Big Data 8(1):1–30. https://doi.org/10.1186/s40537-021-00445-7

    Article  Google Scholar 

  4. Grace K, Salvatier J, Dafoe A, Zhang B, Evans O (2018) When will AI exceed human performance? Evidence from AI experts. J Artif Intell Res 62:729–754. https://doi.org/10.48550/ar**v.1705.08807

    Article  MathSciNet  Google Scholar 

  5. Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, ..., Zhang J (2021) Artificial intelligence: a powerful paradigm for scientific research. Innovation 2(4): 100179. https://doi.org/10.1016/j.xinn.2021.100179

  6. Kuzlu M, Fair C, Guler O (2021) Role of artificial intelligence in the Internet of Things (IoT) cybersecurity. Discover Internet Things 1(1):1–14. https://doi.org/10.1007/s43926-020-00001-4

    Article  Google Scholar 

  7. Zgank A (2021) IoT-based bee swarm activity acoustic classification using deep neural networks. Sensors 21(3):676. https://doi.org/10.3390/s21030676

    Article  Google Scholar 

  8. Churcher A, Ullah R, Ahmad J, Ur Rehman, S, Masood, F, Gogate, M, ..., Buchanan, WJ (2021) An experimental analysis of attack classification using machine learning in IoT networks. Sensors 21(2): 446. https://doi.org/10.3390/s21020446

  9. Sriram GK (2022) The evolution of AI cloud computing and the future it holds. Int Res J Modern Eng Technol Sci 4(2):776–787. e-ISSN: 2582–5208. https://www.researchgate.net/publication/358633514_THE_EVOLUTION_OF_AI_CLOUD_COMPUTING_AND_THE_FUTURE_IT_HOLDS

  10. Kumari S, Abhishek R, Panda BS (2013) Intelligent computing relating to cloud computing. Int J Mech Eng Comput Appl (IJMCA) 1(1): 5–8. https://www.researchgate.net/publication/266023699_Intelligent_Computing_Relating_to_Cloud_Computing

  11. Varzeghani HN, Samadyar Z (2014) Intelligent agents: a comprehensive survey. Inte J Electron Commun Comput Eng 5(4):790–798. ISSN 2249–071X. https://www.researchgate.net/publication/264436271_Intelligent_Agents_A_Comprehensive_Survey

  12. Adetiba E, John T, Akinrinmade A, Moninuola F, Akintade O, Badejo J (2021) Evolution of artificial intelligence languages, a systematic literature review. ar**v:2101.11501. https://doi.org/10.48550/ar**v.2101.11501

  13. Bobrow DG, Raphael B (1974) New programming languages for artificial intelligence research. ACM Comput Surv (CSUR) 6(3):153–174. https://doi.org/10.1145/356631.356632

    Article  Google Scholar 

  14. Zhang Z, Liu Y, Han C, Guo T, Yao T, Mei T (2022) Generalized one-shot domain adaption of generative adversarial networks. ar**v:2209.03665. https://doi.org/10.48550/ar**v.2209.03665

  15. Radford A, Kim JW, Xu T, Brockman G, McLeavey C, Sutskever I (2022) Robust speech recognition via large-scale weak supervision. Technical report. OpenAI. https://cdn.openai.com/papers/whisper.pdf

  16. Glaese A, McAleese N, Trębacz M, Aslanides J, Firoiu V, Ewalds T, ..., Irving G (2022) Improving alignment of dialogue agents via targeted human judgments. ar**v:2209.14375. https://doi.org/10.48550/ar**v.2209.14375

  17. Choudhury S, Moret M, Salvy P, Weilandt D, Hatzimanikatis V, Miskovic L (2022) Reconstructing kinetic models for dynamical studies of metabolism using generative adversarial networks. Nat Mach Intell 4:710–719. https://doi.org/10.1038/s42256-022-00519-y

    Article  Google Scholar 

  18. Pandi A et al (2022) A versatile active learning workflow for optimization of genetic and metabolic networks. Nat Commun 13:3876. https://doi.org/10.1038/s41467-022-31245-z

    Article  Google Scholar 

  19. Hausladen MM, Zhao B, Kubala MS, Francis LF, Kowalewski TM, Ellison CJ (2022) Synthetic growth by self-lubricated photopolymerization and extrusion inspired by plants and fungi. Proc Natl Acad Sci 119(33):e2201776119. https://doi.org/10.1073/pnas.2201776119

    Article  Google Scholar 

  20. Singer U, Polyak A, Hayes T, Yin X, An J, Zhang S, ..., Taigman Y (2022) Make-a-video: text-to-video generation without text-video data. ar**v: 2209:14792. https://doi.org/10.48550/ar**v.2209.14792

  21. Thambawita V, Isaksen JL, Hicks SA, Ghouse J, Ahlberg G, Linneberg A, ..., Kanters JK (2021) DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine. Sci Rep 11(1): 1–8. https://doi.org/10.1038/s41598-021-01295-2

  22. Wang X, Li Y, Zhang H, Shan Y (2021) Towards real-world blind face restoration with generative facial prior. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition:9168–9178. https://doi.org/10.48550/ar**v.2101.04061

  23. Maharana A, Hannan D, Bansal M (2022) StoryDALL-E: adapting pretrained text-to-image transformers for story continuation. ar**v:2209:06192. https://doi.org/10.48550/ar**v.2209.06192

  24. Abbasi NI, Spitale M, Anderson J, Ford T, Jones PB, Gunes H (2022) Can robots help in the evaluation of mental well-being in children? An empirical study. In: 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) IEEE, 1459–1466. https://doi.org/10.1109/RO-MAN53752.2022.9900843

  25. Ellis K, Albright A, Solar-Lezama A, Tenenbaum JB, O’Donnell TJ (2022) Synthesizing theories of human language with Bayesian program induction. Nat Commun 13(1):1–13. https://doi.org/10.1038/s41467-022-32012-w

    Article  Google Scholar 

  26. Kim SW, Zhou Y, Philion J, Torralba A, Fidler S (2020) Learning to simulate dynamic environments with GameGAN. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition:1231–1240. https://doi.org/10.48550/ar**v.2005.12126

  27. Haenlein M, Kaplan A (2019) A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif Manage Rev 61(4):5–14. https://doi.org/10.1177/0008125619864925

    Article  Google Scholar 

  28. Mijwil MM (2015) History of artificial intelligence: 1–5. https://www.ijcai.org/Proceedings/77-2/Papers/083.pdf

  29. Kitchenham B (2004) Procedures for performing systematic reviews. Keele Univ, Keele, 1–26. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=29890a936639862f45cb9a987dd599dce9759bf5

  30. Okoli C (2015) A guide to conducting a standalone systematic literature review. Commun Assoc Inf Syst 37: 43–52. https://hal.science/hal-01574600/

  31. Vom Brocke J, Simons A, Niehaves B, Riemer K, Plattfaut R, & Cleven A (2009) Reconstructing the giant: on the importance of rigour in documenting the literature search process. ECIS. Verona: 17th European Conference on Information Systems (ECIS). https://aisel.aisnet.org/ecis2009/161

  32. Kitchenham BA (2012) Systematic review in software engineering: where we are and where we should be going. In: Proceedings of the 2nd international Workshop on Evidential assessment of software technologies. pp 1–2. https://doi.org/10.1145/2372233.2372235

  33. Leidner D, Kayworth T (2006) A review of culture in information systems research: toward a theory of information technology culture conflict. MIS Q 30(2): 357–399. https://www.jstor.org/stable/25148735

  34. Dybå T, Dingsøyr T (2008) Strength of evidence in systematic reviews in software engineering. In: Proceedings of the Second ACM-IEEE international symposium on empirical software engineering and measurement. 178–187. https://doi.org/10.1145/1414004.1414034

  35. Levy Y, Ellis TJ (2006) A systems approach to conduct an effective literature review in support of information systems research. Inform Sci .9:181–212. https://doi.org/10.28945/479

  36. Gusenbauer M, Haddaway NR (2020) Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar. PubMed and 26 other resources. Res Synth Methods 11(2):181–217. https://doi.org/10.1002/jrsm.1378

    Article  Google Scholar 

  37. Lopez-Cozar ED, Orduna-Malea E, Martín-Martín A (2019) Google Scholar as a data source for research assessment. Springer handbook of science and technology indicators. Springer: 95–127. https://doi.org/10.48550/ar**v.1806.04435

  38. Ciancarini P, Farina M, Okonicha O, Smirnova M, Succi G (2023) Software as storytelling: a systematic literature review. Comput Sci Rev 47:100517. https://doi.org/10.1016/j.cosrev.2022.100517

    Article  MathSciNet  Google Scholar 

  39. Paul J, Khatri P, Kaur Duggal H (2023) Frameworks for develo** impactful systematic literature reviews and theory building: what, why and how? J Decis Syst:1–14. https://doi.org/10.1080/12460125.2023.2197700

  40. Martín-Martín A, Thelwall M, Orduna-Malea E, Delgado López-Cózar E (2021) Google scholar, microsoft academic, scopus, dimensions, web of science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations. Scientometrics 126(1):871–906. https://doi.org/10.1007/s11192-020-03690-4

    Article  Google Scholar 

  41. Fabiana AP, Rogério M (2023) Map** the use of google scholar in evaluative bibliometric or scientometric studies: a bibliometric review. Quant Sci Stud 4(1):233–245. https://doi.org/10.1162/qss_a_00231

    Article  Google Scholar 

  42. Ye H, Li GY, Juang BHF (2019) Deep reinforcement learning based resource allocation for V2V communications. IEEE Trans Veh Technol 68(4):3163–3173. https://doi.org/10.1109/TVT.2019.2897134

    Article  Google Scholar 

  43. Szczepanski M (2020) Is data the new oil? Competition issues in the digital economy. https://www.europarl.europa.eu/thinktank/en/document/EPRS_BRI(2020)646117

  44. Pham QV, Pham XQ, Nguyen TT, Han Z, Kim DS (2022) Artificial intelligence for the metaverse: a survey. https://www.researchgate.net/publication/353887583_ARTIFICIAL_INTELLIGENCE_FOR_CYBERSECURITY_A_SYSTEMATIC_MAPPING_OF_LITERATURE

  45. Brundage M, Avin S, Clark J, Toner H, Eckersley P, Garfinkel B, Dafoe A, Scharre P, Zeitzoff T, Filar B, Anderson H (2018) The malicious use of artificial intelligence: forecasting, prevention, and mitigation. ar**v preprint ar**v:1802.07228. 2018 Feb 20. https://arxiv.org/ftp/arxiv/papers/1802/1802.07228.pdf

  46. Economist (2018) The challenger: technoplotics. https://www.economist.com/briefing/2018/03/15/the-challenger

  47. Lin J, Zhu L, Chen WM, Wang WC, Gan C, Han S (2022) On-device training under 256KB memory. ar**v: 2206:15472. https://doi.org/10.48550/ar**v.2206.15472

  48. Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future Healthc J 6(2):94. https://doi.org/10.7861/futurehosp.6-2-94

    Article  Google Scholar 

  49. Wani SUD, Khan NA, Thakur G, Gautam SP, Ali M, Alam P, ..., Shakeel F (2022) Utilization of artificial intelligence in disease prevention: diagnosis, treatment, and implications for the healthcare workforce. Healthcare 10(4): 608. https://doi.org/10.3390/healthcare10040608. (MDPI)

  50. Utermohlen K (2018) Four robotic process automation (RPA) applications in the healthcare industry. Medium. https://medium.com/@karl.utermohlen/4-robotic-process-automation-rpa-applications-in-the-healthcare-industry-4d449b24b613

  51. Huang H, Hwang GJ, Jong MSY (2022) Technological solutions for promoting employees’ knowledge levels and practical skills: an SVVR-based blended learning approach for professional training. Comput Educ 189:104593. https://doi.org/10.1016/j.compedu.2022.104593. (ISSN:0360-1315)

    Article  Google Scholar 

  52. Ali A (2021) Artificial intelligence potential trends in military. Foundation Univ J Eng Appl Sci 2(1):20–30. https://doi.org/10.33897/fujeas.v2i1.380. (HEC Recognized Y Category ISSN 2706-7351)

    Article  Google Scholar 

  53. Beck J, Rainoldi M, Egger R (2019) Virtual reality in tourism: a state-of-the-art review. Tour Rev 74(3):586–612. https://doi.org/10.1108/TR-03-2017-0049

    Article  Google Scholar 

  54. Ma Y, Wang Z, Yang H, Yang L (2020) Artificial intelligence applications in the development of autonomous vehicles: a survey. IEEE/CAA J Autom Sin 7(2):315–329. https://doi.org/10.1109/JAS.2020.1003021

    Article  Google Scholar 

  55. De Vries K (2020) You never fake alone. Creative AI in action. Inf Commun Soc 23(14):2110–2127. https://doi.org/10.1080/1369118X.2020.1754877

    Article  Google Scholar 

  56. Davenport TH, Glaser J (2002) Just-in-time delivery comes to knowledge management. Harvard Business Review. https://hbr.org/2002/07/just-in-time-delivery-comes-to-knowledge-management

  57. Hayashi H, Abe K, Uchida S (2019) GlyphGAN: style-consistent font generation based on generative adversarial networks. Knowl-Based Syst 186:104927. https://doi.org/10.48550/ar**v.1905.12502

    Article  Google Scholar 

  58. Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition: 8110–8119. https://doi.org/10.48550/ar**v.1912.04958

  59. Joji LE, Kanjirappally K, Joseph G (2022) Grey coloured anime character. In: Proceedings of the National Conference on Emerging Computer Applications (NCECA). 4(1):89. https://nceca.in/2022/21_Grey_Coloured_Anime_Character.pdf

  60. Balasubramanian S, Balasubramanian VN (2019) Teaching gans to sketch in vector format. https://doi.org/10.48550/ar**v.1904.03620

  61. Epstein Z, Levine S, Rand DG, Rahwan I (2020) Who gets credit for ai-generated art? Iscience 23(9):101515. https://doi.org/10.1016/j.isci.2020.101515

    Article  Google Scholar 

  62. Ramesh A, Dhariwal P, Nichol A, Chu C, Chen M (2022) Hierarchical text-conditional image generation with clip latents. ar**v:2204:06125. https://doi.org/10.48550/ar**v.2204.06125

  63. Yu J, Park S, Kwon SH, Ho CMB, Pyo CS, Lee H (2020) AI-based stroke disease prediction system using real-time electromyography signals. Appl Sci 10(19):6791. https://doi.org/10.3390/app10196791

    Article  Google Scholar 

  64. Qi H, Fuin N, Cruz G, Pan J, Kuestner T, Bustin A, Botnar RM, Prieto C (2021) Non-rigid respiratory motion estimation of wholeheart coronary MR images using unsupervised deep learning. IEEE Trans Med Imaging 40(1):444–454. https://doi.org/10.1109/TMI.2020.3029205

    Article  Google Scholar 

  65. Wu P, Ding W, You Z, An P (2019) Virtual reality video quality assessment based on 3D convolutional neural networks. In: Proc. IEEE International Conference on Image Processing (ICIP) Taipei, Taiwan, 3187–3191. https://doi.org/10.1109/ICIP.2019.8803023

  66. Barriga NA, Stanescu M, Besoain F, Buro M (2019) Improving RTS game AI by supervised policy learning, tactical search, and deep reinforcement learning. IEEE Comput Intell Mag 14(3):8–18. https://doi.org/10.1109/MCI.2019.2919363

    Article  Google Scholar 

  67. Liu H, Zhang S, Zhang P, Zhou X, Shao X, Pu G, Zhang Y (2021) Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing. IEEE Trans Veh Technol 70(6):6073–6084. https://doi.org/10.1109/TVT.2021.3076780

    Article  Google Scholar 

  68. Tanwar S, Bhatia Q, Patel P, Kumari A, Singh PK, Hong WC (2019) Machine learning adoption in blockchain-based smart applications: the challenges, and a way forward. IEEE Access 8:474–488. https://doi.org/10.1109/ACCESS.2019.2961372

    Article  Google Scholar 

  69. Guo S, Lin Y, Li S, Chen Z, Wan H (2019) Deep spatial–temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans Intell Transp Syst 20(10):3913–3926. https://doi.org/10.1109/TITS.2019.2906365

    Article  Google Scholar 

  70. Park S, Cha HS, Kwon J, Kim H, Im CH (2020) Development of an online home appliance control system using augmented reality and an ssvep-based brain-computer interface. In: 2020 8th International Winter Conference on Brain-Computer Interface (BCI) IEEE, 1–2 https://doi.org/10.1109/ACCESS.2019.2952613

  71. Jeong JH, Shim KH, Kim DJ, Lee SW (2020) Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals. IEEE Trans Neural Syst Rehabil Eng 28(5):1226–1238. https://doi.org/10.1109/TNSRE.2020.2981659

    Article  Google Scholar 

  72. Sharma R, Morwal S, Agarwal B, Chandra R, Khan MS (2020) A deep neural network-based model for named entity recognition for Hindi language. Neural Comput Appl 32:16191–16203. https://doi.org/10.1007/s00521-020-04881-z

    Article  Google Scholar 

  73. Young T, Hazarika D, Poria S, Cambria E (2018) Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag 13(3):55–75. https://doi.org/10.48550/ar**v.1708.02709

    Article  Google Scholar 

  74. Bhat SA, Huang NF (2021) Big data and AI revolution in precision agriculture: survey and challenges. IEEE Access 9:110 209-110 222. https://doi.org/10.1109/ACCESS.2021.3102227

    Article  Google Scholar 

  75. Shwartz-Ziv R, Armon A (2022) Tabular data: deep learning is not all you need. Inf Fusion 81:84–90. https://doi.org/10.48550/ar**v.2106.03253

    Article  Google Scholar 

  76. Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, **e S (2022) A convnet for the 2020s. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition:11976–11986. https://doi.org/10.1109/CVPR52688.2022.01167

  77. Lin T, Wang Y, Liu X, Qiu X (2021) A survey of transformers. ar**v:2106.04554. https://doi.org/10.48550/ar**v.2106.04554

  78. Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning. PMLR: 1597–1607. https://doi.org/10.48550/ar**v.2002.05709

  79. Chen H, Wang Y, Xu C, Shi B, Xu C, Tian Q, Xu C (2020) AdderNet: do we really need multiplications in deep learning? In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition:1468–1477. https://doi.org/10.48550/ar**v.1912.13200

  80. Chollet F (2019) On the measure of intelligence. ar**v:1911.01547. https://doi.org/10.48550/ar**v.1911.01547

  81. Došilović FK, Brčić M, Hlupić N (2018) Explainable artificial intelligence: a survey. In: 2018 41st international convention on information and communication technology. electronics and microelectronics (MIPRO) IEEE, 0210–0215 https://doi.org/10.23919/MIPRO.2018.8400040

  82. Jiang AQ, Welleck S, Zhou JP, Li W, Liu J, Jamnik M, ..., Lample G (2022) Draft, sketch, and prove: guiding formal theorem provers with informal proofs. ar**v preprint ar**v:2210.12283. https://doi.org/10.48550/ar**v.2210.12283

  83. Huang J, Gu SS, Hou L, Wu Y, Wang X, Yu H, Han J (2022) large language models can self-improve. ar**v preprint ar**v:2210.11610. https://openreview.net/forum?id=NiEtU7blzN

  84. Yang K, Peng N, Tian Y, Klein D (2022) Re3: generating longer stories with recursive reprompting and revision. ar**v:2210.06774. https://doi.org/10.48550/ar**v.2210.06774

  85. Kreuzberger D, Kühl N, Hirschl S (2022) Machine Learning Operations (MLOps): overview, definition, and architecture. ar**v:2205.02302. https://doi.org/10.48550/ar**v.2205.02302

  86. Lin J, Zhu L, Chen WM, Wang WC, Gan C, Han S (2022) On-device training under 256KB memory. ar**v preprint ar**v:2206.15472. https://doi.org/10.48550/ar**v.2206.15472

  87. Koizumi Y, Yatebe K, Zen H, Bacchiani M (2022) WaveFit: an iterative and non-autoregressive neural vocoder based on fixed-point iteration. ar**v:2210.01029. Audio and speech processing. https://doi.org/10.48550/ar**v.2210.01029

  88. Lewis S, Pavlasek J, Jenkis OC (2022) NARF22: neural articulated radiance fields for configuration-aware rendering. ar**v:2210.01166v1. https://arxiv.org/abs/2210.01166

  89. Hess P et al (2022) Physically constrained generative adversarial networks for improving precipitation fields from earth system models. Nat Mach Intell. https://doi.org/10.1038/s42256-022-00540-1

    Article  Google Scholar 

  90. Witowski J, Heacock L, Reig B, Kang SK, Lewin A, Pysarenko K, ..., Geras KJ (2022) Improving breast cancer diagnostics with deep learning for MRI. Sci Transl Med 14(664). eabo4802. https://www.science.org/doi/abs/10.1126/scitranslmed.abo4802

  91. Chowdhury M, Sadek AW (2012) Advantages and limitations of artificial intelligence. Artif Intell Appl Critical Transport Issues 6(3): 360–375. https://onlinepubs.trb.org/onlinepubs/circulars/ec168.pdf#page=14

  92. Prabha C, Singh J, Rasool R (2022). AIoT technologies and applications for smart environments. https://doi.org/10.1049/PBPC057E

    Article  Google Scholar 

  93. Dhiman P, Kaur A, Bonkra A (2023) Fake information detection using deep learning methods: a survey. In: 2023 International Conference on Artificial Intelligence and Smart Communication (AISC):858–863. IEEE. https://doi.org/10.1109/AISC56616.2023.10085519

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India

    Neha Sharma

  2. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, 147004, Punjab, India

    Neeru **dal

Authors
  1. Neha Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neeru **dal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Neha Sharma.

Ethics declarations

Conflict of interest

The authors have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, N., **dal, N. Emerging artificial intelligence applications: metaverse, IoT, cybersecurity, healthcare - an overview. Multimed Tools Appl 83, 57317–57345 (2024). https://doi.org/10.1007/s11042-023-17890-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17890-6

Keywords

Search

Navigation